Alternatives to Amazon SageMaker Pipelines logo

Alternatives to Amazon SageMaker Pipelines

Jenkins, Travis CI, CircleCI, GitHub Actions, and TensorFlow are the most popular alternatives and competitors to Amazon SageMaker Pipelines.
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What is Amazon SageMaker Pipelines and what are its top alternatives?

It is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.
Amazon SageMaker Pipelines is a tool in the Machine Learning Tools category of a tech stack.

Top Alternatives to Amazon SageMaker Pipelines

  • Jenkins
    Jenkins

    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project. ...

  • Travis CI
    Travis CI

    Free for open source projects, our CI environment provides multiple runtimes (e.g. Node.js or PHP versions), data stores and so on. Because of this, hosting your project on travis-ci.com means you can effortlessly test your library or applications against multiple runtimes and data stores without even having all of them installed locally. ...

  • CircleCI
    CircleCI

    Continuous integration and delivery platform helps software teams rapidly release code with confidence by automating the build, test, and deploy process. Offers a modern software development platform that lets teams ramp. ...

  • GitHub Actions
    GitHub Actions

    It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want. ...

  • TensorFlow
    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • GitLab CI
    GitLab CI

    GitLab offers a continuous integration service. If you add a .gitlab-ci.yml file to the root directory of your repository, and configure your GitLab project to use a Runner, then each merge request or push triggers your CI pipeline. ...

  • PyTorch
    PyTorch

    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...

  • TeamCity
    TeamCity

    TeamCity is a user-friendly continuous integration (CI) server for professional developers, build engineers, and DevOps. It is trivial to setup and absolutely free for small teams and open source projects. ...

Amazon SageMaker Pipelines alternatives & related posts

Jenkins logo

Jenkins

47.5K
39.8K
2.2K
An extendable open source continuous integration server
47.5K
39.8K
+ 1
2.2K
PROS OF JENKINS
  • 520
    Hosted internally
  • 464
    Free open source
  • 314
    Great to build, deploy or launch anything async
  • 243
    Tons of integrations
  • 210
    Rich set of plugins with good documentation
  • 110
    Has support for build pipelines
  • 72
    Open source and tons of integrations
  • 65
    Easy setup
  • 62
    It is open-source
  • 54
    Workflow plugin
  • 11
    Configuration as code
  • 10
    Very powerful tool
  • 9
    Many Plugins
  • 9
    Continuous Integration
  • 8
    Great flexibility
  • 8
    Git and Maven integration is better
  • 7
    100% free and open source
  • 6
    Github integration
  • 6
    Slack Integration (plugin)
  • 5
    Easy customisation
  • 5
    Self-hosted GitLab Integration (plugin)
  • 4
    Docker support
  • 4
    Pipeline API
  • 3
    Platform idnependency
  • 3
    Excellent docker integration
  • 3
    Fast builds
  • 3
    Hosted Externally
  • 2
    Customizable
  • 2
    AWS Integration
  • 2
    It's Everywhere
  • 2
    JOBDSL
  • 2
    Can be run as a Docker container
  • 2
    It`w worked
  • 1
    Easily extendable with seamless integration
  • 1
    Build PR Branch Only
  • 1
    NodeJS Support
  • 1
    PHP Support
  • 1
    Ruby/Rails Support
  • 1
    Universal controller
  • 1
    Loose Coupling
CONS OF JENKINS
  • 12
    Workarounds needed for basic requirements
  • 9
    Groovy with cumbersome syntax
  • 7
    Plugins compatibility issues
  • 6
    Lack of support
  • 6
    Limited abilities with declarative pipelines
  • 4
    No YAML syntax
  • 3
    Too tied to plugins versions

related Jenkins posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 5.1M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

See more
Thierry Schellenbach

Releasing new versions of our services is done by Travis CI. Travis first runs our test suite. Once it passes, it publishes a new release binary to GitHub.

Common tasks such as installing dependencies for the Go project, or building a binary are automated using plain old Makefiles. (We know, crazy old school, right?) Our binaries are compressed using UPX.

Travis has come a long way over the past years. I used to prefer Jenkins in some cases since it was easier to debug broken builds. With the addition of the aptly named “debug build” button, Travis is now the clear winner. It’s easy to use and free for open source, with no need to maintain anything.

#ContinuousIntegration #CodeCollaborationVersionControl

See more
Travis CI logo

Travis CI

11.6K
5.9K
1.8K
A hosted continuous integration service for open source and private projects
11.6K
5.9K
+ 1
1.8K
PROS OF TRAVIS CI
  • 506
    Github integration
  • 388
    Free for open source
  • 272
    Easy to get started
  • 191
    Nice interface
  • 163
    Automatic deployment
  • 72
    Tutorials for each programming language
  • 40
    Friendly folks
  • 29
    Support for multiple ruby versions
  • 28
    Osx support
  • 24
    Easy handling of secret keys
  • 6
    Fast builds
  • 4
    Support for students
  • 3
    The best tool for Open Source CI
  • 3
    Hosted
  • 3
    Build Matrices
  • 2
    Integrates with everything
  • 2
    Github Pull Request build
  • 2
    Straightforward Github/Coveralls integration
  • 2
    Easy of Usage
  • 1
    Caching resolved artifacts
  • 1
    Docker support
  • 1
    Great Documentation
  • 1
    Free for students
  • 1
    Build matrix
  • 1
    No-brainer for CI
  • 1
    Debug build workflow
  • 1
    Ubuntu trusty is not supported
  • 1
    Configuration saved with project repository
  • 1
    Multi-threaded run
  • 1
    Hipchat Integration
  • 0
    One
  • 0
    Perfect
CONS OF TRAVIS CI
  • 8
    Can't be hosted insternally
  • 3
    Feature lacking
  • 3
    Unstable
  • 2
    Incomplete documentation for all platforms

related Travis CI posts

Thierry Schellenbach

Releasing new versions of our services is done by Travis CI. Travis first runs our test suite. Once it passes, it publishes a new release binary to GitHub.

Common tasks such as installing dependencies for the Go project, or building a binary are automated using plain old Makefiles. (We know, crazy old school, right?) Our binaries are compressed using UPX.

Travis has come a long way over the past years. I used to prefer Jenkins in some cases since it was easier to debug broken builds. With the addition of the aptly named “debug build” button, Travis is now the clear winner. It’s easy to use and free for open source, with no need to maintain anything.

#ContinuousIntegration #CodeCollaborationVersionControl

See more
Tim Abbott
Shared insights
on
Travis CITravis CICircleCICircleCI
at

We actually started out on Travis CI, but we've migrated our main builds to CircleCI, and it's been a huge improvement.

The reason it's been a huge improvement is that Travis CI has a fundamentally bad design for their images, where they start with a standard base Linux image containing tons of packages (several versions of postgres, every programming language environment, etc). This is potentially nice for the "get builds for a small project running quickly" use case, but it's a total disaster for a larger project that needs a decent number of dependencies and cares about the performance and reliability of their build.

This issue is exacerbated by their networking infrastructure being unreliable; we usually saw over 1% of builds failing due to transient networking errors in Travis CI, even after we added retries to the most frequently failing operations like apt update or pip install. And they never install Ubuntu's point release updates to their images. So doing an apt update, apt install, or especially apt upgrade would take forever. We ended up writing code to actually uninstall many of their base packages and pin the versions of hundreds of others to get a semi-fast, semi-reliable build. It was infuriating.

The CircleCI v2.0 system has the right design for a CI system: we can customize the base image to start with any expensive-to-install packages we need for our build, and we can update that image if and when we want to. The end result is that when migrating, we were able to delete all the hacky optimizations mentioned above, while still ending up with a 50% faster build latency. And we've also had 5-10x fewer issues with networking-related flakes, which means one doesn't have to constantly check whether a build failure is actually due to an issue with the code under test or "just another networking flake".

See more
CircleCI logo

CircleCI

10.2K
5.9K
962
Automate your development process quickly, safely, and at scale
10.2K
5.9K
+ 1
962
PROS OF CIRCLECI
  • 224
    Github integration
  • 176
    Easy setup
  • 152
    Fast builds
  • 94
    Competitively priced
  • 74
    Slack integration
  • 54
    Docker support
  • 44
    Awesome UI
  • 33
    Great customer support
  • 18
    Ios support
  • 14
    Hipchat integration
  • 13
    SSH debug access
  • 11
    Free for Open Source
  • 5
    Mobile support
  • 5
    Bitbucket integration
  • 4
    Nodejs support
  • 4
    AWS CodeDeploy integration
  • 3
    Free for Github private repo
  • 3
    Great support
  • 3
    YAML configuration
  • 2
    Continuous Deployment
  • 2
    Simple, clean UI
  • 2
    Clojure
  • 2
    Clojurescript
  • 2
    OSX support
  • 1
    Android support
  • 1
    Fair pricing
  • 1
    All inclusive testing
  • 1
    Japanese in rspec comment appears OK
  • 1
    Unstable
  • 1
    Build PR Branch Only
  • 1
    Autoscaling
  • 1
    Helpful documentation
  • 1
    Really easy to use
  • 1
    Favorite
  • 1
    Works
  • 1
    Easy to deploy to private servers
  • 1
    Extremely configurable
  • 1
    So circular
  • 1
    Easy setup, easy to understand, fast and reliable
  • 1
    Parallel builds for slow test suites
  • 1
    Easy setup. 2.0 is fast!
  • 1
    Parallelism
  • 0
    Stable
  • 0
    Stable
CONS OF CIRCLECI
  • 12
    Unstable
  • 6
    Scammy pricing structure
  • 0
    Aggressive Github permissions

related CircleCI posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 5.1M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

See more
Tim Abbott
Shared insights
on
Travis CITravis CICircleCICircleCI
at

We actually started out on Travis CI, but we've migrated our main builds to CircleCI, and it's been a huge improvement.

The reason it's been a huge improvement is that Travis CI has a fundamentally bad design for their images, where they start with a standard base Linux image containing tons of packages (several versions of postgres, every programming language environment, etc). This is potentially nice for the "get builds for a small project running quickly" use case, but it's a total disaster for a larger project that needs a decent number of dependencies and cares about the performance and reliability of their build.

This issue is exacerbated by their networking infrastructure being unreliable; we usually saw over 1% of builds failing due to transient networking errors in Travis CI, even after we added retries to the most frequently failing operations like apt update or pip install. And they never install Ubuntu's point release updates to their images. So doing an apt update, apt install, or especially apt upgrade would take forever. We ended up writing code to actually uninstall many of their base packages and pin the versions of hundreds of others to get a semi-fast, semi-reliable build. It was infuriating.

The CircleCI v2.0 system has the right design for a CI system: we can customize the base image to start with any expensive-to-install packages we need for our build, and we can update that image if and when we want to. The end result is that when migrating, we were able to delete all the hacky optimizations mentioned above, while still ending up with a 50% faster build latency. And we've also had 5-10x fewer issues with networking-related flakes, which means one doesn't have to constantly check whether a build failure is actually due to an issue with the code under test or "just another networking flake".

See more
GitHub Actions logo

GitHub Actions

3.9K
567
20
Automate your workflow from idea to production
3.9K
567
+ 1
20
PROS OF GITHUB ACTIONS
  • 4
    Integration with GitHub
  • 4
    Free
  • 3
    Easy to duplicate a workflow
  • 2
    Docker Support
  • 2
    Read actions in Marketplace
  • 2
    Ready actions in Marketplace
  • 1
    Fast
  • 1
    Configs stored in .github
  • 1
    Active Development Roadmap
CONS OF GITHUB ACTIONS
  • 5
    Lacking [skip ci]
  • 4
    Lacking allow failure
  • 3
    Lacking job specific badges
  • 2
    No ssh login to servers
  • 1
    No Deployment Projects
  • 1
    No manual launch

related GitHub Actions posts

Somnath Mahale
Engineering Leader at Altimetrik Corp. · | 8 upvotes · 118.6K views

I am in the process of evaluating CircleCI, Drone.io, and Github Actions to cover my #CI/ CD needs. I would appreciate your advice on comparative study w.r.t. attributes like language-Inclusive support, code-base integration, performance, cost, maintenance, support, ease of use, ability to deal with big projects, etc. based on actual industry experience.

Thanks in advance!

See more
Omkar Kulkarni
DevOps Engineer at LTI · | 3 upvotes · 36.3K views
Shared insights
on
GitLabGitLabGitHub ActionsGitHub Actions

Hello Everyone, Can some please help me to understand the difference between GitHub Actions And GitLab I have been trying to understand them, but still did not get how exactly they are different.

See more
TensorFlow logo

TensorFlow

3K
3.1K
80
Open Source Software Library for Machine Intelligence
3K
3.1K
+ 1
80
PROS OF TENSORFLOW
  • 26
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 3
    High level abstraction
  • 2
    Powerful
  • 2
    Easy to use
CONS OF TENSORFLOW
  • 9
    Hard
  • 6
    Hard to debug
  • 1
    Documentation not very helpful

related TensorFlow posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

See more
GitLab CI logo

GitLab CI

2K
1.4K
71
GitLab integrated CI to test, build and deploy your code
2K
1.4K
+ 1
71
PROS OF GITLAB CI
  • 22
    Robust CI with awesome Docker support
  • 12
    Simple configuration
  • 8
    All in one solution
  • 6
    Source Control and CI in one place
  • 5
    Easy to configure own build server i.e. GitLab-Runner
  • 5
    Integrated with VCS on commit
  • 4
    Free and open source
  • 2
    Hosted internally
  • 1
    Pipeline could be started manually
  • 1
    Built-in support of Review Apps
  • 1
    Enable or disable pipeline by using env variables
  • 1
    Gitlab templates could be shared across logical group
  • 1
    Easy to setup the dedicated runner to particular job
  • 1
    Built-in Docker Registry
  • 1
    Built-in support of Kubernetes
CONS OF GITLAB CI
  • 2
    Works best with GitLab repositories

related GitLab CI posts

Joshua Dean Küpper
CEO at Scrayos UG (haftungsbeschränkt) · | 20 upvotes · 362K views

We use GitLab CI because of the great native integration as a part of the GitLab framework and the linting-capabilities it offers. The visualization of complex pipelines and the embedding within the project overview made Gitlab CI even more convenient. We use it for all projects, all deployments and as a part of GitLab Pages.

While we initially used the Shell-executor, we quickly switched to the Docker-executor and use it exclusively now.

We formerly used Jenkins but preferred to handle everything within GitLab . Aside from the unification of our infrastructure another motivation was the "configuration-in-file"-approach, that Gitlab CI offered, while Jenkins support of this concept was very limited and users had to resort to using the webinterface. Since the file is included within the repository, it is also version controlled, which was a huge plus for us.

See more
Sebastian Dellwig
Tech Lead at Porsche Digital GmbH · | 6 upvotes · 152.6K views
Shared insights
on
GitLab CIGitLab CICircleCICircleCICodeshipCodeship

We are using GitLab CI and were very happy with it. The integration of all tools like CI/CD, tickets, etc makes it very easy to stay on top of things. But be aware, Gitlab currently does not have iOS build support. So if you want to exchange that for CircleCI / Codeship to have to invest some effort. We are using a managed Mac OS device and installed the Gitlab runner there, to have iOS builds.

See more
PyTorch logo

PyTorch

1.1K
1.2K
42
A deep learning framework that puts Python first
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PROS OF PYTORCH
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CONS OF PYTORCH
  • 3
    Lots of code

related PyTorch posts

Server side

We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

  • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

  • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

  • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

Client side

  • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

  • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

  • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

Cache

  • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

Database

  • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

Infrastructure

  • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

Other Tools

  • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

  • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber · | 8 upvotes · 1.4M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

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TeamCity

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TeamCity is an ultimate Continuous Integration tool for professionals
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PROS OF TEAMCITY
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    On premise
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    Github integration
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    Great UI
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    Smart
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    Can run jobs in parallel
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    Free for open source
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    Crossplatform
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    Chain dependencies
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    REST API
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    Great support by jetbrains
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    Fully-functional out of the box
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    Projects hierarchy
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    100+ plugins
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    Free for small teams
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    Per-project permissions
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    Personal notifications
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    Artifact dependencies
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    Smart build failure analysis and tracking
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    Build progress messages promoting from running process
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    Built-in artifacts repository
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    Powerful build chains / pipelines
  • 1
    TeamCity Professional is FREE
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    Repository-stored, full settings dsl with ide support
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    Official reliable support
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    Hosted internally
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    High-Availability
CONS OF TEAMCITY
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    User friendly
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    High costs for more than three build agents
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related TeamCity posts

Tymoteusz Paul
Devops guy at X20X Development LTD · | 23 upvotes · 5.1M views

Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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Sarah Elson
Product Growth at LambdaTest · | 4 upvotes · 366K views

@producthunt LambdaTest Selenium JavaScript Java Python PHP Cucumber TeamCity CircleCI With this new release of LambdaTest automation, you can run tests across an Online Selenium Grid of 2000+ browsers and OS combinations to perform cross browser testing. This saves you from the pain of maintaining the infrastructure and also saves you the licensing costs for browsers and operating systems. #testing #Seleniumgrid #Selenium #testautomation #automation #webdriver #producthunt hunted

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